38 lines
1.3 KiB
R
38 lines
1.3 KiB
R
% Generated by roxygen2 (4.0.1): do not edit by hand
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\docType{methods}
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\name{predict,xgb.Booster-method}
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\alias{predict,xgb.Booster-method}
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\title{Predict method for eXtreme Gradient Boosting model}
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\usage{
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\S4method{predict}{xgb.Booster}(object, newdata, outputmargin = FALSE,
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ntreelimit = NULL)
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}
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\arguments{
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\item{object}{Object of class "xgb.Boost"}
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\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or
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\code{xgb.DMatrix}.}
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\item{outputmargin}{whether the prediction should be shown in the original
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value of sum of functions, when outputmargin=TRUE, the prediction is
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untransformed margin value. In logistic regression, outputmargin=T will
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output value before logistic transformation.}
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\item{ntreelimit}{limit number of trees used in prediction, this parameter is
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only valid for gbtree, but not for gblinear. set it to be value bigger
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than 0. It will use all trees by default.}
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}
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\description{
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Predicted values based on xgboost model object.
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}
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\examples{
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nround = 2,objective = "binary:logistic")
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pred <- predict(bst, test$data)
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}
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